Магистратура
2024/2025
Научно-исследовательский семинар "Вычислительные науки"
Статус:
Курс по выбору (Когнитивные науки и технологии: от нейрона к познанию)
Направление:
37.04.01. Психология
Кто читает:
Институт когнитивных нейронаук
Где читается:
Институт когнитивных нейронаук
Когда читается:
2-й курс, 1, 2 модуль
Формат изучения:
без онлайн-курса
Охват аудитории:
для своего кампуса
Прогр. обучения:
Когнитивные науки и технологии: от нейрона к познанию
Язык:
английский
Кредиты:
6
Course Syllabus
Abstract
The research seminar “Research Seminar: Computational Sciences” consists of two parts: “Advanced data analysis of neurophysiological data” and «Dynamical theory of neural activity». The first part provides the understanding of algorithmic pipelines routinely used in the analysis of EEG and MEG data. Given the quick development of analysis tools, it is always challenging to fully comprehend the machinery hidden behind the typical button-press toolbox packages. Instead of approaching data analysis packages as a “black box”, at the end of the course the students will be able to fully comprehend the meaning of their choices while setting options in their data analysis workflow. During this course, we will go through the details of data acquisition, data processing and step by step implementation of most advanced data analysis pipeline and the understanding of the main parameters involved. After quickly reviewing the physical principles of signal acquisition and introducing some mathematical tools, the course dives into the main topics of time-frequency analysis, functional connectivity and statistical analysis. The second part “Dynamical theory of neural activity” aims to introduce masters graduate students to basic theory of dynamical systems as applied to neurodynamics: mathematical modeling of neuronal activity and techniques of model analysis. This shall prove to be useful for the students who interested in learning computational features of brain neuronal populations. During the course we are going to consider how neurons perform information processing and learn how to develop mathematical descriptions of these phenomena. We will start from the classification of different types of neuronal behavior and show the ways to motivate model choice as well as relations between the features of neuronal activity and dynamical properties of the models. Then we will explore different techniques to study the neuronal models: evolution of the phase portraits, bifurcation analysis, parameters showing level of synchronization, etc. Starting from the single neuron models we will proceed to the mathematical description of neuronal ensembles, impact of neuronal intrinsic activity and network connection topology in collective neuronal dynamics. We will study methods for estimation of network synchrony among other measures of network activity. The research seminar provides the students with the basic theory of neurophysiological data analysis and dynamical basis of neuronal activity which are useful not only in neuroscience and cognitive sciences but also in other scientific areas using similar mathematical framework.